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Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control

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4 Author(s)
Chi-Hsu Wang ; Sch. of Microelectron. Eng., Griffith Univ., Nathan, Qld., Australia ; Wei-Yen Wang ; Tsu-Tian Lee ; Pao-Shun Tseng

A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:25 ,  Issue: 5 )